
import pandas as pd
import numpy as np
url = "https://docs.google.com/spreadsheets/d/1mS6khEB6m8cPNenNvY9Tg6bJ6YkmcvCI/export?format=csv&gid=1283335856"
df = pd.read_csv(url)
df.head()


important_columns = ["SUBJECT", "NBR", "COURSE NAME", "PROF", "TOTAL", "A+", "A", "A-", "B+", "B", "B-", "C+", "C", "C-", "D", "F", "W", "AVG GPA"]

def gpa_letter_converter(gpa):
    letter_grades = {
        "A": 4.0, 
        "A-": (3.7, 3.8, 3.9), 
        "B+": (3.3, 3.4, 3.5, 3.6), 
        "B": (3.0, 3.1, 3.2), 
        "B-": (2.7, 2.8, 2.9), 
        "C+": (2.3, 2.4, 2.5, 2.6),
        "C": (2.0, 2.1, 2.2), 
        "C-": (1.7, 1.8, 1.9),
        "D": (1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6),
        "F": (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)
    }
    for letter_grade, number_grade in letter_grades.items():
        if isinstance(number_grade, float) and gpa == number_grade:
            return letter_grade
        elif isinstance(number_grade, tuple) and gpa in number_grade:
            return letter_grade
    
    return None

analysis_df = df[important_columns]
analysis_df.head()


analysis_df = analysis_df.dropna(subset = "PROF")
analysis_df = analysis_df[analysis_df["AVG GPA"] != 0]
analysis_df.shape

(2333, 18)


cs_df = analysis_df[analysis_df["SUBJECT"] == "CSCI"]
pd.set_option('display.max_rows', None)
cs_df


def calculate_average_gpas(df):
    # Prepare a list to store the results
    results = []

    # GPA equivalents for each letter grade
    letter_grades_to_gpa = {
        "A+": 4.0,
        "A": 4.0,
        "A-": 3.7,
        "B+": 3.3,
        "B": 3.0,
        "B-": 2.7,
        "C+": 2.3,
        "C": 2.0,
        "C-": 1.7,
        "D": 1.0,
        "F": 0.0
    }

    # Loop over all unique course numbers
    for class_nbr in df["NBR"].unique():
        # Filter the DataFrame for the current course number
        df_nbr = df[df["NBR"] == class_nbr]

        # Loop over all unique professors for the current course number
        for prof in df_nbr["PROF"].unique():
            # Filter the DataFrame for the current professor
            df_prof = df_nbr[df_nbr["PROF"] == prof]

            # Calculate the average GPA for the current professor and course number
            avg_gpa_prof = round(df_prof["AVG GPA"].mean(), 1)

            # Convert the individual grade counts to GPA equivalents and calculate the standard deviation
            gpa_distributions = []
            for grade_letter, gpa in letter_grades_to_gpa.items():
                gpa_distributions.extend([gpa] * df_prof[grade_letter].sum())
            std_dev_gpa_prof = round(np.std(gpa_distributions), 2)

            # Append the result to the list
            results.append({
                "CLASS NUMBER": class_nbr,
                "PROF": prof,
                "AVG GPA PROF": avg_gpa_prof,
                "AVG GPA PROF LETTER": gpa_letter_converter(avg_gpa_prof),
                "STD DEV GPA PROF": std_dev_gpa_prof
            })

        # If there is more than one professor for this course, calculate the average GPA for the current course number, regardless of the professor
        if df_nbr["PROF"].nunique() > 1:
            avg_gpa_nbr = round(df_nbr["AVG GPA"].mean(), 1)

            # Convert the individual grade counts to GPA equivalents and calculate the standard deviation
            gpa_distributions = []
            for grade_letter, gpa in letter_grades_to_gpa.items():
                gpa_distributions.extend([gpa] * df_nbr[grade_letter].sum())
            std_dev_gpa_nbr = round(np.std(gpa_distributions), 2)

            # Append the result to the list
            results.append({
                "CLASS NUMBER": class_nbr,
                "PROF": "All Professors",
                "AVG GPA PROF": avg_gpa_nbr,
                "AVG GPA PROF LETTER": gpa_letter_converter(avg_gpa_nbr),
                "STD DEV GPA PROF": std_dev_gpa_nbr
            })

    # Convert the list of results to a DataFrame
    df_results = pd.DataFrame(results)
    
    # Find the hardest class based on average GPA
    hardest_class = df_results[df_results["PROF"] == "All Professors"].sort_values("AVG GPA PROF").iloc[0]

    # Calculate the average GPA for the entire subject
    avg_gpa_subject = round(df["AVG GPA"].mean(), 1)

    print(f"Average GPA for this entire subject in Fall 2022 is: {avg_gpa_subject}, which is a {gpa_letter_converter(avg_gpa_subject)}")
    print(f"The hardest class based on average GPA in Fall 2022 is class number: {hardest_class['CLASS NUMBER']} with an average GPA of {hardest_class['AVG GPA PROF']}, which is a {hardest_class['AVG GPA PROF LETTER']}")
    print("\nStandard deviation tells us about the spread of the grades that students received in each class. A higher standard deviation indicates a wider range of grades, while a lower standard deviation indicates that grades were more closely clustered around the average.")

    return df_results

cs_df_results = calculate_average_gpas(cs_df)
cs_df_results

Average GPA for this entire subject in Fall 2022 is: 2.5, which is a C+
The hardest class based on average GPA in Fall 2022 is class number: 211 with an average GPA of 1.7, which is a C-

Standard deviation tells us about the spread of the grades that students received in each class. A higher standard deviation indicates a wider range of grades, while a lower standard deviation indicates that grades were more closely clustered around the average.


import matplotlib.pyplot as plt
import seaborn as sns

plt.figure()
all_prof_df_results = cs_df_results[cs_df_results["PROF"] == 'All Professors']
sns.histplot(all_prof_df_results["STD DEV GPA PROF"], kde=True, color='skyblue')
plt.axvline(all_prof_df_results["STD DEV GPA PROF"].mean(), color='red', linestyle='dashed', linewidth=1, label='Mean')
min_ylim, max_ylim = plt.ylim()
plt.text(all_prof_df_results["STD DEV GPA PROF"].mean()*1.12, max_ylim*0.9, 'Mean: {:.2f}'.format(all_prof_df_results["STD DEV GPA PROF"].mean()))

# Generate a color palette with as many colors as classes
colors = sns.color_palette("husl", len(all_prof_df_results))

for idx, (_, row) in enumerate(all_prof_df_results.iterrows()):
    plt.axvline(row["STD DEV GPA PROF"], color=colors[idx], linestyle='dotted', linewidth=0.5, label=row["CLASS NUMBER"])

plt.title("Distribution of Standard Deviations of GPAs")
plt.xlabel("Standard Deviation")
plt.ylabel("Frequency")
plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1))  # Adjusted the location to ensure it doesn't overlap with the plot
plt.show()


def calculate_teacher_gpas(df):
    # Prepare a list to store the results
    results = []

    # GPA equivalents for each letter grade
    letter_grades_to_gpa = {
        "A+": 4.0,
        "A": 4.0,
        "A-": 3.7,
        "B+": 3.3,
        "B": 3.0,
        "B-": 2.7,
        "C+": 2.3,
        "C": 2.0,
        "C-": 1.7,
        "D": 1.0,
        "F": 0.0
    }

    # Loop over all unique professors
    for prof in sorted(df["PROF"].unique()):
        # Filter the DataFrame for the current professor
        df_prof = df[df["PROF"] == prof]

        # Calculate the average GPA for the current professor
        avg_gpa_prof = round(df_prof["AVG GPA"].mean(), 1)

        # Convert the individual grade counts to GPA equivalents and calculate the standard deviation
        gpa_distributions = []
        for grade_letter, gpa in letter_grades_to_gpa.items():
            gpa_distributions.extend([gpa] * df_prof[grade_letter].sum())
        std_dev_gpa_prof = round(np.std(gpa_distributions), 1)

        # Calculate the percentage of students who withdrew for the current professor
        withdraw_percentage = df_prof["W"].sum() / df_prof["TOTAL"].sum()

        # Append the result to the list
        results.append({
            "PROF": prof,
            "AVG GPA PROF": avg_gpa_prof,
            "AVG GPA PROF LETTER": gpa_letter_converter(avg_gpa_prof),
            "STD DEV GPA PROF": std_dev_gpa_prof,
            "NUM OF CLASSES": len(df_prof),
            "WITHDRAW PERCENTAGE": round(withdraw_percentage * 100, 1)
        })

    # Convert the list of results to a DataFrame
    df_results = pd.DataFrame(results)

    # Calculate the average GPA for the entire subject
    avg_gpa_subject = round(df["AVG GPA"].mean(), 1)

    # Calculate the average standard deviation for the entire subject
    gpa_distributions = []
    for grade_letter, gpa in letter_grades_to_gpa.items():
        gpa_distributions.extend([gpa] * df[grade_letter].sum())
    avg_std_dev_subject = round(np.std(gpa_distributions), 1)
    
    # Calculate the average withdrawal percentage for the entire subject
    withdraw_percentage_subject = round((df["W"].sum() / df["TOTAL"].sum()) * 100, 2)
    
    # Find the professors who teach more than one class, have a GPA that is the same or higher than the subject average GPA,
    # and for whom less than 40% of students withdrew
    best_profs = df_results[(df_results["NUM OF CLASSES"] > 1) & (df_results["AVG GPA PROF"] >= avg_gpa_subject) & (df_results["WITHDRAW PERCENTAGE"] <= withdraw_percentage_subject)]

    print(f"Professors who teach more than one class, have a GPA that is the same or higher than the subject average GPA ({avg_gpa_subject}), and have a withdrawal percentage that is less or equal than the subject average withdrawal percentage ({withdraw_percentage_subject}%):\n")
    print(best_profs, "\n")
    print("Note: This ignores rate my professor ratings. This is soley based on GPA and it doesn't consider how much students actually learn from these teachers!")
    print(f"Average standard deviation of GPA for all teachers in this subject in Fall 2022 is: {avg_std_dev_subject}")

    return df_results

teacher_cs_df_results = calculate_teacher_gpas(cs_df)
teacher_cs_df_results

Professors who teach more than one class, have a GPA that is the same or higher than the subject average GPA (2.5), and have a withdrawal percentage that is less or equal than the subject average withdrawal percentage (22.26%):

            PROF  AVG GPA PROF AVG GPA PROF LETTER  STD DEV GPA PROF  \
9       FRIED, M           3.3                  B+               0.6   
10   GOLDBERG, R           3.0                   B               0.8   
12  GREENBERG, A           3.1                   B               0.8   
16  KAHROBAEI, D           3.1                   B               1.1   
23     RAHMAN, M           3.0                   B               0.8   
28  STEINBERG, O           2.6                  C+               1.1   
32        TAO, X           3.0                   B               1.1   
37        YEH, J           2.8                  B-               1.0   

    NUM OF CLASSES  WITHDRAW PERCENTAGE  
9                2                  8.0  
10               3                 14.3  
12               3                  6.4  
16               2                  9.9  
23               2                  1.7  
28               4                  6.5  
32               4                 10.1  
37               3                 14.1   

Note: This ignores rate my professor ratings. This is soley based on GPA and it doesn't consider how much students actually learn from these teachers!
Average standard deviation of GPA for all teachers in this subject in Fall 2022 is: 1.3


import plotly.express as px

# Set the overall average GPA
mean_gpa = 2.5

# Create the scatter plot using Plotly Express
fig = px.scatter(teacher_cs_df_results, 
                 x=list(range(len(teacher_cs_df_results))),
                 y='AVG GPA PROF',
                 hover_name='PROF', # This will show the professor's name when hovering over a point
                 title="CS Professor GPA Averages vs CS Subject Average GPA",
                 labels={'x': 'Professor (By Index Above)', 'y': 'Average GPA'},
                 size_max=100)

# Add a line for the average GPA
fig.add_shape(
    type='line',
    line=dict(dash='dash', color='red'),
    x0=0,
    x1=len(teacher_cs_df_results),
    y0=mean_gpa,
    y1=mean_gpa,
)

# Show the plot
fig.show()


# Count the number of classes with an average GPA at or greater than 3.0 and those less than 3.0
green_percentage = teacher_cs_df_results[teacher_cs_df_results['AVG GPA PROF'] >= 3.0].shape[0]
red_percentage = teacher_cs_df_results[teacher_cs_df_results['AVG GPA PROF'] < 3.0].shape[0]

# Create the values and labels for the pie chart
values = [green_percentage, red_percentage]
labels = ['At or above 3.0 (B or above)', 'At or below 2.7 (B- or below)']

# Define the colors for each section (green and red)
colors = ['#77dd77', '#ff6961']

# Plot the pie chart
plt.figure(figsize = (6, 6))
plt.pie(values, labels = labels, colors = colors, autopct = '%1.1f%%')

# Set the title
plt.title("Average GPA of all CS Teachers")

# Show the plot
plt.show()

	TERM	SUBJECT	NBR	COURSE NAME	SECTION	PROF	TOTAL	A+	A	A-	...	C	C-	D	F	W	INC/NA	AVG GPA	join the best club on campus	Unnamed: 22	https://discord.gg/vxCqd4hdak
0	F2022	ACCT	100	Fin & Mgr Acct	1	HO, V	28	0	11	2	...	3	0	0	0	5	0	3.383	NaN	NaN	NaN
1	F2022	ACCT	101	Intro Thry & Prac of Acct I	11	CHAN, J	29	0	13	2	...	0	1	0	0	3	0	3.464	NaN	NaN	NaN
2	F2022	ACCT	101	Intro Thry & Prac of Acct I	5	RUTHIZER, S	50	2	9	14	...	1	0	3	2	3	0	3.176	NaN	NaN	NaN
3	F2022	ACCT	101	Intro Thry & Prac of Acct I	2	GRUZA, M	45	1	2	3	...	0	0	0	0	25	0	3.067	NaN	NaN	NaN
4	F2022	ACCT	101	Intro Thry & Prac of Acct I	4	FEISULLIN, A	45	4	6	1	...	9	1	1	0	4	1	2.908	NaN	NaN	NaN

	SUBJECT	NBR	COURSE NAME	PROF	TOTAL	A+	A	A-	B+	B	B-	C+	C	C-	D	F	W	AVG GPA
0	ACCT	100	Fin & Mgr Acct	HO, V	28	0	11	2	1	3	3	0	3	0	0	0	5	3.383
1	ACCT	101	Intro Thry & Prac of Acct I	CHAN, J	29	0	13	2	0	4	5	0	0	1	0	0	3	3.464
2	ACCT	101	Intro Thry & Prac of Acct I	RUTHIZER, S	50	2	9	14	6	4	5	0	1	0	3	2	3	3.176
3	ACCT	101	Intro Thry & Prac of Acct I	GRUZA, M	45	1	2	3	1	5	0	6	0	0	0	0	25	3.067
4	ACCT	101	Intro Thry & Prac of Acct I	FEISULLIN, A	45	4	6	1	7	5	0	6	9	1	1	0	4	2.908

	SUBJECT	NBR	COURSE NAME	PROF	TOTAL	A+	A	A-	B+	B	B-	C+	C	C-	D	F	W	AVG GPA
582	CSCI	12	Intro Computers & Computation	WAXMAN, J	58	0	2	7	5	14	6	2	2	1	3	5	10	2.594
585	CSCI	48	Spreadsheet Programming	FRIED, M	131	19	10	23	28	17	12	4	1	0	1	1	12	3.354
586	CSCI	48	Spreadsheet Programming	JAGDEO, M	94	7	14	15	9	11	6	1	1	0	7	7	14	2.945
596	CSCI	100	Information and Intelligence	CASTRO, R	40	0	8	4	5	5	3	0	4	2	0	3	6	2.876
597	CSCI	111	Intro Algorithmic Problem Solv	SCHLEY, R	236	10	30	14	8	22	8	17	25	8	13	22	49	2.494
598	CSCI	111	Intro Algorithmic Problem Solv	CHYN, X	239	0	19	10	8	39	14	11	8	0	23	26	75	2.269
599	CSCI	111	Intro Algorithmic Problem Solv	CHYN, X	240	0	21	11	7	28	22	7	13	0	23	26	68	2.255
619	CSCI	112	Intro Algori Prob Solving Java	CONNOR, T	13	6	2	1	1	1	1	0	0	0	0	0	1	3.725
621	CSCI	211	Object-Oriented Program in C++	WAXMAN, J	150	9	9	7	7	10	10	4	16	0	15	30	32	2.002
622	CSCI	211	Object-Oriented Program in C++	WAXMAN, J	105	5	5	4	3	4	11	2	9	0	15	25	22	1.735
623	CSCI	211	Object-Oriented Program in C++	ALAYEV, Y	16	0	1	0	0	0	0	0	1	0	1	2	11	1.400
635	CSCI	212	Object-Oriented Prog in Java	TSE, C	36	1	6	1	3	3	0	3	5	0	0	3	11	2.700
636	CSCI	212	Object-Oriented Prog in Java	LORD, K	161	5	6	11	11	13	13	10	12	9	9	21	39	2.220
637	CSCI	212	Object-Oriented Prog in Java	LORD, K	127	7	12	5	7	11	7	4	6	3	12	24	29	2.120
648	CSCI	220	Discrete Structures	KAHROBAEI, D	39	0	18	3	3	2	2	0	5	4	0	0	2	3.276
649	CSCI	220	Discrete Structures	KAHROBAEI, D	42	0	14	3	1	3	2	0	5	5	0	3	6	2.869
650	CSCI	220	Discrete Structures	CHYN, X	88	0	8	4	8	6	12	7	11	0	1	13	17	2.324
651	CSCI	220	Discrete Structures	CHYN, X	58	1	6	2	4	9	7	3	11	0	0	12	3	2.244
652	CSCI	220	Discrete Structures	CESARETTI, P	30	0	3	3	1	2	1	2	4	0	0	7	7	2.074
653	CSCI	240	Computer Org & Assembly Lang	YEH, J	45	2	7	3	4	7	7	3	3	3	0	2	4	2.883
654	CSCI	240	Computer Org & Assembly Lang	YEH, J	45	5	1	5	3	7	4	1	3	6	0	1	8	2.853
655	CSCI	240	Computer Org & Assembly Lang	YEH, J	45	0	4	3	4	5	5	7	2	4	0	4	7	2.518
656	CSCI	240	Computer Org & Assembly Lang	SVITAK, J	31	0	2	0	3	3	2	2	4	2	2	2	8	2.286
657	CSCI	240	Computer Org & Assembly Lang	SVITAK, J	26	0	2	3	1	3	0	1	4	0	4	2	6	2.285
658	CSCI	240	Computer Org & Assembly Lang	SVITAK, J	37	0	4	4	1	4	0	1	3	2	5	4	9	2.243
659	CSCI	313	Data Structures	TAO, X	39	9	6	2	8	5	3	0	0	0	0	4	2	3.159
660	CSCI	313	Data Structures	TAO, X	36	0	5	1	6	16	1	0	1	0	0	1	5	3.103
661	CSCI	313	Data Structures	TAO, X	24	2	2	2	1	6	1	0	0	0	0	6	4	2.370
662	CSCI	313	Data Structures	TSAI, C	10	1	0	0	1	0	0	0	1	0	0	1	6	2.325
663	CSCI	313	Data Structures	STEINBERG, O	31	3	2	0	4	1	3	3	3	3	6	2	1	2.277
664	CSCI	313	Data Structures	SMITH-THOMPSON, A	29	3	3	0	0	1	0	1	6	0	0	5	8	2.174
665	CSCI	313	Data Structures	STEINBERG, O	42	2	1	2	1	4	4	2	8	4	4	5	5	2.078
666	CSCI	313	Data Structures	SMITH-THOMPSON, A	25	0	2	0	0	1	1	1	6	0	0	3	11	2.000
667	CSCI	316	Principles of Programming Lang	SMITH-THOMPSON, A	35	0	5	0	2	9	6	4	6	0	0	0	1	2.844
668	CSCI	316	Principles of Programming Lang	KONG, T	35	3	6	1	3	3	3	1	4	0	1	4	5	2.690
669	CSCI	316	Principles of Programming Lang	KONG, T	35	1	1	3	3	4	3	1	3	0	1	5	9	2.336
670	CSCI	316	Principles of Programming Lang	SVITAK, J	28	0	0	1	0	5	1	1	3	2	7	2	6	1.823
671	CSCI	316	Principles of Programming Lang	SVITAK, J	30	0	0	0	2	1	1	2	4	2	6	3	9	1.633
672	CSCI	316	Principles of Programming Lang	KONG, T	61	0	5	0	2	4	3	3	4	0	2	23	13	1.383
673	CSCI	320	Theory of Computation	OBRENIC, B	86	2	11	2	7	11	10	7	12	0	0	15	9	2.371
674	CSCI	320	Theory of Computation	BOKLAN, K	37	1	2	0	0	0	0	0	2	0	0	2	30	2.286
675	CSCI	320	Theory of Computation	OBRENIC, B	109	0	6	0	4	19	9	6	19	0	1	24	21	1.947
676	CSCI	320	Theory of Computation	BOKLAN, K	28	0	1	0	0	0	1	0	3	0	0	4	19	1.411
677	CSCI	323	Design & Analysis Algorithms	TAO, X	30	7	5	7	3	3	1	0	2	0	0	0	2	3.554
678	CSCI	323	Design & Analysis Algorithms	PHILLIPS, T	18	1	0	4	1	0	1	1	3	0	0	1	6	2.758
679	CSCI	323	Design & Analysis Algorithms	PHILLIPS, T	25	2	2	1	0	2	2	0	1	0	0	4	11	2.364
680	CSCI	323	Design & Analysis Algorithms	BROWN, T	30	0	0	1	3	4	8	1	6	0	0	5	2	2.196
681	CSCI	323	Design & Analysis Algorithms	GOSWAMI, M	61	1	5	6	2	5	1	6	7	0	0	13	15	2.137
682	CSCI	331	Database Systems	STEINBERG, O	35	4	4	3	4	6	3	3	4	3	1	0	0	2.954
683	CSCI	331	Database Systems	STEINBERG, O	31	1	2	4	6	3	4	3	1	3	1	0	3	2.907
684	CSCI	331	Database Systems	HELLER, P	29	0	1	1	1	0	1	2	5	0	0	1	17	2.358
685	CSCI	331	Database Systems	LEAVITT, D	39	1	4	4	3	2	4	1	4	0	1	7	8	2.348
686	CSCI	331	Database Systems	OBRENIC, B	83	3	3	0	2	6	9	7	29	0	1	13	10	2.027
687	CSCI	334	Data Mining and Warehousing	SY, B	13	0	1	2	0	2	0	1	1	0	0	0	6	3.100
688	CSCI	335	Information Org and Retrieval	GOLDBERG, R	33	0	4	4	4	5	3	2	5	0	0	1	5	2.918
689	CSCI	340	Operating Systems Principles	SVADLENKA, J	33	2	3	1	1	3	3	2	1	1	0	0	14	3.082
690	CSCI	340	Operating Systems Principles	SVADLENKA, J	22	1	2	0	3	2	1	0	3	0	1	0	9	2.892
691	CSCI	340	Operating Systems Principles	SVADLENKA, J	37	1	3	3	2	1	5	4	1	1	1	2	13	2.671
692	CSCI	340	Operating Systems Principles	SVADLENKA, J	29	2	2	1	3	3	2	1	1	0	2	2	10	2.647
693	CSCI	340	Operating Systems Principles	FLUTURE, S	37	0	0	4	4	3	7	6	3	0	0	2	8	2.610
694	CSCI	340	Operating Systems Principles	SVADLENKA, J	33	2	0	1	0	2	3	3	2	2	4	1	13	2.205
695	CSCI	340	Operating Systems Principles	FLUTURE, S	37	0	1	2	0	0	4	5	7	0	0	3	13	2.168
696	CSCI	343	Computer Architecture	RAHMAN, M	34	2	2	6	3	9	5	5	1	0	0	1	0	3.003
697	CSCI	343	Computer Architecture	FLUTURE, S	31	2	4	0	0	2	1	2	2	0	0	2	16	2.753
698	CSCI	343	Computer Architecture	FLUTURE, S	70	1	9	2	3	1	4	5	2	0	0	5	38	2.706
699	CSCI	343	Computer Architecture	SMITH-THOMPSON, A	32	3	4	4	4	2	1	1	3	0	0	6	3	2.607
700	CSCI	343	Computer Architecture	UPADHYAY, V	32	0	2	2	1	1	0	2	10	0	0	1	13	2.437
701	CSCI	343	Computer Architecture	SMITH-THOMPSON, A	32	2	3	4	2	3	0	0	5	0	0	8	5	2.237
702	CSCI	343	Computer Architecture	UPADHYAY, V	32	1	0	1	1	1	0	4	8	0	0	9	7	1.568
703	CSCI	344	Distributed Systems	FLUTURE, S	22	1	1	0	0	1	2	2	3	0	0	1	10	2.455
704	CSCI	348	Data Communications	RAHMAN, M	26	0	2	5	3	6	3	1	4	0	0	1	1	2.912
705	CSCI	355	Internet and Web Technologies	FRIED, M	45	0	8	5	10	7	9	4	0	0	0	0	2	3.209
706	CSCI	355	Internet and Web Technologies	LAW, R	24	2	2	1	1	4	1	3	3	0	0	1	6	2.811
707	CSCI	355	Internet and Web Technologies	LAW, R	22	2	1	2	2	2	0	1	1	0	0	5	5	2.269
708	CSCI	355	Internet and Web Technologies	LAW, R	33	2	2	1	2	2	1	1	2	0	0	10	10	1.796
709	CSCI	370	Software Engineering	GREENBERG, A	29	1	8	0	5	9	0	1	0	1	0	0	4	3.340
710	CSCI	370	Software Engineering	GREENBERG, A	35	0	10	0	7	11	1	1	3	0	1	0	1	3.179
711	CSCI	370	Software Engineering	GREENBERG, A	30	1	5	1	5	6	1	5	4	0	0	1	1	2.910
712	CSCI	370	Software Engineering	ABREU, A	32	0	3	1	5	5	6	4	1	0	1	5	1	2.439
713	CSCI	370	Software Engineering	ABREU, A	21	0	1	0	2	2	0	2	1	3	5	1	4	1.959
714	CSCI	381	VT: Special Topics in Comp Sci	PHILLIPS, T	16	2	4	4	0	0	1	1	0	0	0	1	3	3.369
715	CSCI	381	VT: Special Topics in Comp Sci	HELLER, P	33	1	8	0	7	2	5	2	1	0	0	0	7	3.277
716	CSCI	381	VT: Special Topics in Comp Sci	RODAY, R	39	2	7	7	3	7	4	1	4	0	0	0	4	3.254
717	CSCI	381	VT: Special Topics in Comp Sci	WAXMAN, J	30	11	3	3	1	1	1	1	3	0	0	2	4	3.246
718	CSCI	381	VT: Special Topics in Comp Sci	GOLDBERG, R	32	0	5	3	4	7	2	3	3	0	0	0	5	3.096
719	CSCI	381	VT: Special Topics in Comp Sci	GOLDBERG, R	33	0	10	2	3	0	4	3	6	0	1	0	4	3.034
720	CSCI	381	VT: Special Topics in Comp Sci	TSAI, C	16	0	1	0	0	3	1	1	0	0	0	1	9	2.571
721	CSCI	381	VT: Special Topics in Comp Sci	ROZOVSKAYA, A	20	0	1	0	2	3	0	2	6	0	0	1	5	2.413
722	CSCI	381	VT: Special Topics in Comp Sci	GRYAK, J	29	0	1	1	0	4	4	0	3	0	0	3	13	2.281
723	CSCI	381	VT: Special Topics in Comp Sci	ROZOVSKAYA, A	29	0	1	1	5	1	3	1	3	0	0	5	9	2.180
724	CSCI	381	VT: Special Topics in Comp Sci	BROWN, T	27	0	0	0	2	2	4	1	12	0	0	3	3	2.071

	CLASS NUMBER	PROF	AVG GPA PROF	AVG GPA PROF LETTER	STD DEV GPA PROF
0	12	WAXMAN, J	2.6	C+	1.13
1	48	FRIED, M	3.4	B+	0.62
2	48	JAGDEO, M	2.9	B-	1.25
3	48	All Professors	3.1	B	0.95
4	100	CASTRO, R	2.9	B-	1.14
5	111	SCHLEY, R	2.5	C+	1.30
6	111	CHYN, X	2.3	C+	1.34
7	111	All Professors	2.3	C+	1.33
8	112	CONNOR, T	3.7	A-	0.44
9	211	WAXMAN, J	1.9	C-	1.47
10	211	ALAYEV, Y	1.4	D	1.50
11	211	All Professors	1.7	C-	1.47
12	212	TSE, C	2.7	B-	1.25
13	212	LORD, K	2.2	C	1.40
14	212	All Professors	2.3	C+	1.39
15	220	KAHROBAEI, D	3.1	B	1.09
16	220	CHYN, X	2.3	C+	1.31
17	220	CESARETTI, P	2.1	C	1.52
18	220	All Professors	2.6	C+	1.32
19	240	YEH, J	2.8	B-	1.01
20	240	SVITAK, J	2.3	C+	1.26
21	240	All Professors	2.5	C+	1.14
22	313	TAO, X	2.9	B-	1.21
23	313	TSAI, C	2.3	C+	1.52
24	313	STEINBERG, O	2.2	C	1.17
25	313	SMITH-THOMPSON, A	2.1	C	1.42
26	313	All Professors	2.4	C+	1.30
27	316	SMITH-THOMPSON, A	2.8	B-	0.63
28	316	KONG, T	2.1	C	1.54
29	316	SVITAK, J	1.7	C-	1.01
30	316	All Professors	2.1	C	1.35
31	320	OBRENIC, B	2.2	C	1.34
32	320	BOKLAN, K	1.8	C-	1.58
33	320	All Professors	2.0	C	1.36
34	323	TAO, X	3.6	B+	0.57
35	323	PHILLIPS, T	2.6	C+	1.41
36	323	BROWN, T	2.2	C	1.12
37	323	GOSWAMI, M	2.1	C	1.49
38	323	All Professors	2.6	C+	1.36
39	331	STEINBERG, O	2.9	B-	0.81
40	331	HELLER, P	2.4	C+	0.99
41	331	LEAVITT, D	2.3	C+	1.46
42	331	OBRENIC, B	2.0	C	1.12
43	331	All Professors	2.5	C+	1.16
44	334	SY, B	3.1	B	0.70
45	335	GOLDBERG, R	2.9	B-	0.87
46	340	SVADLENKA, J	2.7	B-	1.09
47	340	FLUTURE, S	2.4	C+	0.98
48	340	All Professors	2.6	C+	1.05
49	343	RAHMAN, M	3.0	B	0.77
50	343	FLUTURE, S	2.7	B-	1.34
51	343	SMITH-THOMPSON, A	2.4	C+	1.55
52	343	UPADHYAY, V	2.0	C	1.23
53	343	All Professors	2.5	C+	1.35
54	344	FLUTURE, S	2.5	C+	1.04
55	348	RAHMAN, M	2.9	B-	0.86
56	355	FRIED, M	3.2	B	0.53
57	355	LAW, R	2.3	C+	1.53
58	355	All Professors	2.5	C+	1.30
59	370	GREENBERG, A	3.1	B	0.76
60	370	ABREU, A	2.2	C	1.19
61	370	All Professors	2.8	B-	1.02
62	381	PHILLIPS, T	3.4	B+	1.10
63	381	HELLER, P	3.3	B+	0.62
64	381	RODAY, R	3.3	B+	0.66
65	381	WAXMAN, J	3.2	B	1.17
66	381	GOLDBERG, R	3.1	B	0.78
67	381	TSAI, C	2.6	C+	1.15
68	381	ROZOVSKAYA, A	2.3	C+	1.19
69	381	GRYAK, J	2.3	C+	1.21
70	381	BROWN, T	2.1	C	0.90
71	381	All Professors	2.8	B-	1.06

Data Analysis Presented by:¶

Average GPA for the entire subject of CS in Fall 2022: 2.5, which is a C+, with the hardest subject being CSCI 211 with a 1.7¶

Average Withdrawal Rate for any individual class for the entire subject of CS in Fall 2022 is 22.26% ¶

Nonetheless, if the distribution of scores is approximately normal (i.e., follows a bell curve), the empirical rule states:¶

Bringing it all together:¶

IMPORTANT FUNCTIONS USED IN THIS NOTEBOOK:¶

Note: These will be used for doing the rest of the analysis of subjects by class!¶

SCROLL DOWN FOR DATA VISUALIZATIONS AND A MORE DETAILED WRITTEN NON-TECHNICAL ANALYSIS¶

Also, check this website out to get some basics on formating text with Jupyter notebook!¶

The isinstance() function returns True if the specified object is of the specified type, otherwise False (https://stackoverflow.com/questions/1549801/what-are-the-differences-between-type-and-isinstance)¶

dropna():¶

pd.set_option :¶

pd.reset_option¶

In pandas, both unique and nunique are used to get unique values of a series object, but they serve different purposes and return different types of output:¶

iloc[]:¶

sort_values():¶

Note¶

To actually interact with plot below and be able to hover over plots and see which teacher its referring to, download the html version of this file instead¶

Teacher Analysis¶

Most of the teachers above make sense based off their Rate My Professor:¶

Other's don't:¶

Teachers that missed the mark because of gpa, withdrawal rate, and/or only teaching one class, but they have a rate my professor of 4 and above:¶

Class Description/Analysis¶

The hardest class based on average GPA is class number: 211 with an average GPA of 1.7, which is a C-¶

Professor:¶

Professors:¶

	PROF	AVG GPA PROF	AVG GPA PROF LETTER	STD DEV GPA PROF	NUM OF CLASSES	WITHDRAW PERCENTAGE
0	ABREU, A	2.2	C	1.2	2	9.4
1	ALAYEV, Y	1.4	D	1.5	1	68.8
2	BOKLAN, K	1.8	C-	1.6	2	75.4
3	BROWN, T	2.1	C	1.0	2	8.8
4	CASTRO, R	2.9	B-	1.1	1	15.0
5	CESARETTI, P	2.1	C	1.5	1	23.3
6	CHYN, X	2.3	C+	1.3	4	26.1
7	CONNOR, T	3.7	A-	0.4	1	7.7
8	FLUTURE, S	2.5	C+	1.2	5	43.1
9	FRIED, M	3.3	B+	0.6	2	8.0
10	GOLDBERG, R	3.0	B	0.8	3	14.3
11	GOSWAMI, M	2.1	C	1.5	1	24.6
12	GREENBERG, A	3.1	B	0.8	3	6.4
13	GRYAK, J	2.3	C+	1.2	1	44.8
14	HELLER, P	2.8	B-	0.9	2	38.7
15	JAGDEO, M	2.9	B-	1.3	1	14.9
16	KAHROBAEI, D	3.1	B	1.1	2	9.9
17	KONG, T	2.1	C	1.5	3	20.6
18	LAW, R	2.3	C+	1.5	3	26.6
19	LEAVITT, D	2.3	C+	1.5	1	20.5
20	LORD, K	2.2	C	1.4	2	23.6
21	OBRENIC, B	2.1	C	1.3	3	14.4
22	PHILLIPS, T	2.8	B-	1.4	3	33.9
23	RAHMAN, M	3.0	B	0.8	2	1.7
24	RODAY, R	3.3	B+	0.7	1	10.3
25	ROZOVSKAYA, A	2.3	C+	1.2	2	28.6
26	SCHLEY, R	2.5	C+	1.3	1	20.8
27	SMITH-THOMPSON, A	2.4	C+	1.4	5	18.3
28	STEINBERG, O	2.6	C+	1.1	4	6.5
29	SVADLENKA, J	2.7	B-	1.1	5	38.3
30	SVITAK, J	2.1	C	1.2	5	25.0
31	SY, B	3.1	B	0.7	1	46.2
32	TAO, X	3.0	B	1.1	4	10.1
33	TSAI, C	2.4	C+	1.3	2	57.7
34	TSE, C	2.7	B-	1.2	1	30.6
35	UPADHYAY, V	2.0	C	1.2	2	31.2
36	WAXMAN, J	2.4	C+	1.5	4	19.8
37	YEH, J	2.8	B-	1.0	3	14.1